python进行相关性分析并绘制散点图详解

#1 load pakeage
 
import pandas as pd#读写表格以及表格处理
import numpy as np#用于数据计算
 
import geopandas as gpd
import matplotlib.pyplot as plt
import seaborn as sns
import os
import warnings
warnings.filterwarnings("ignore")
plt.rc('font',family='Times New Roman')
from glob import glob
from osgeo import gdal,gdal_array,gdalnumeric
plt.rcParams['font.sans-serif']=['Simhei']  #显示中文
plt.rcParams['axes.unicode_minus']=False    #显示负号 

df = pd.read_csv('./JXDY_1.csv',encoding='utf-8')
df.head()

df.corr()#默认是pearson相关性分析

a = 2#这里的相关性是从第二位开始进行计算的，所以我从第二位开始提取
for i in df.columns[3:]:
    a1 = sns.lmplot(y='fruit',x=i,data=df)
    ax = plt.GCa()
    ax.text(0.9,1,"Pearson:{:.2f}".fORMat(df.corr().iloc[1,a]),transform=ax.transAxes)#添加相关性
    a = a+1
    plt.savefig('./{}.jpg'.format(i),dpi=300,bbox_inches = 'tight')